Department of MEEM, City University of Hong Kong, Kowloon, Hong Kong.
Clin Neurophysiol. 2010 May;121(5):694-703. doi: 10.1016/j.clinph.2009.12.030. Epub 2010 Jan 22.
Ordinal patterns analysis such as permutation entropy of the EEG series has been found to usefully track brain dynamics and has been applied to detect changes in the dynamics of EEG data. In order to further investigate hidden nonlinear dynamical characteristics in EEG data for differentiating brain states, this paper proposes a novel dissimilarity measure based on the ordinal pattern distributions of EEG series.
Given a segment of EEG series, we first map this series into a phase space, then calculate the ordinal sequences and the distribution of these ordinal patterns. Finally, the dissimilarity between two EEG series can be qualified via a simple distance measure. A neural mass model was proposed to simulate EEG data and test the performance of the dissimilarity measure based on the ordinal patterns distribution. Furthermore, this measure was then applied to analyze EEG data from 24 Genetic Absence Epilepsy Rats from Strasbourg (GAERS), with the aim of distinguishing between interictal, preictal and ictal states.
The dissimilarity measure of a pair of EEG signals within the same group and across different groups was calculated, respectively. As expected, the dissimilarity measures during different brain states were higher than internal dissimilarity measures. When applied to the preictal detection of absence seizures, the proposed dissimilarity measure successfully detected the preictal state prior to their onset in 109 out of 168 seizures (64.9%).
Our results showed that dissimilarity measures between EEG segments during the same brain state were significant smaller that those during different states. This suggested that the dissimilarity measure, based on the ordinal patterns in the time series, could be used to detect changes in the dynamics of EEG data. Moreover, our results suggested that ordinal patterns in the EEG might be a potential characteristic of brain dynamics.
This dissimilarity measure is a promising method to reveal dynamic changes in EEG, for example as occur in the transition of epileptic seizures. This method is simple and fast, so might be applied in designing an automated closed-loop seizure prevention system for absence epilepsy.
脑电系列的排列模式分析，如排列熵，已被证明可有效地跟踪脑动力，并已被应用于检测脑电数据动力的变化。为了进一步研究脑电数据中的隐藏非线性动力特征，以区分脑状态，本文提出了一种基于脑电系列排列模式分布的新的不相似性度量。
给定一段脑电序列，我们首先将该序列映射到相空间中，然后计算这些排列模式的排列序列和分布。最后，通过简单的距离度量来确定两个脑电序列之间的不相似性。提出了一个神经质量模型来模拟脑电数据，并测试基于排列模式分布的不相似性度量的性能。此外，该度量还被应用于分析来自斯特拉斯堡 24 只遗传性失神癫痫大鼠（GAERS）的脑电数据，旨在区分发作间期、发作前期和发作期。
分别计算了同一组和不同组之间一对脑电信号的不相似性度量。正如预期的那样，不同脑状态下的不相似性度量值高于内部不相似性度量值。当应用于失神发作的发作前期检测时，所提出的不相似性度量成功地在 168 次发作中的 109 次（64.9%）发作前检测到发作前期。
我们的结果表明，同一脑状态下脑电段之间的不相似性度量显著小于不同状态下的不相似性度量。这表明，基于时间序列中的排列模式的不相似性度量可以用于检测脑电数据动力的变化。此外，我们的结果表明，脑电中的排列模式可能是脑动力的潜在特征。
这种不相似性度量方法是揭示脑电动态变化的一种很有前途的方法，例如在癫痫发作的转变中。该方法简单快速，因此可能应用于设计用于失神性癫痫的自动闭环发作预防系统。
